I get the use of fever charts and buffers in CCPM. What I am struggling with is how these would apply when utilizing the Kanban Board and usage of Monte Carlo simulations.
In CCPM we reduce the estimates by 1/2, then apply the remainder of the estimate as a buffer. Then we track our actual task completions and monitor how far our end date is pushing into the buffer. Green/Yellow/Red tells us how we are doing and whether we need to start taking action.
The above has two requirements:
- there is an estimate for each task
- The tasks are tracked individually.
I have been using Dan Vacanti’s tool to determine when my projects will be done. There are no estimates for each deliverable. We simply track each deliverable as it works across the Kanban board and record the date it is completed. Focus is on maintaining Flow and monitoring the age of our Work In Progress to insure work items don’t stagnate. Each day/week we record how many work items completed. This establishes our throughput distribution which is used to feed the Monte Carlo simulation.
Once we have several items completed we can begin the simulations, realizing that our throughput history is not very good. The Monte Carlo simulation takes random samples from this historical throughput history and applies that value as an predicted throughput value for the next day in the future. This is repeated for each day into the future until there is nothing left in our backlog for the project. This is our predicted date for the project to complete, but it is just one data point.
The process above is repeated 10,000 times which generates a distribution that looks something like this. Our red zone indicates that if we are promising a date that falls in this range we are living on the edge. If we are promising a date that falls in the green range, we are in much better shape.
Each week, our throughput distribution is updated with the results of the past week. Assuming no material change has occurred to our “system”, these data points are included in the throughput distribution with all previous completed items and this “new” distribution is used to feed our next Monte Carlo simulation. The new simulation now has new data, and less items to complete, and the simulation is recorded from today’s date forward. Again, we get a distribution of 10,000 samples showing what the new probability is for completing by the date we promised our customer.
The big difference here is that I don’t see a need for the buffer and tracking with a fever chart. I simply continue to feed the simulations each week with the latest throughput data and monitor the date I am most likely to complete the project. This calculation of 10,000 samples takes less than a minute. If I am really feeling frisky, I run the simulations for a million samples which takes less than two minutes.
I feel this is better than fever charts as the estimates (notoriously bad) don’t come into play. We utilize the modern tools to continuous monitor our delivery dates. As we collect more and more data, our risk continues to reduce and we start to zero in on our true completion date. We still leverage the Red/Green/Yellow, but in a different way.
We simply ask the teams to focus on Flow and minimize queueing time as much as possible. We rely on the team’s knowledge of the work to properly sequence the work and monitor the throughput output to determine how we are doing the and where we stand.